Systems and methods for priority-based optimization of data element utilization

ABSTRACT

Systems and methods are disclosed for optimizing distribution of resources to data elements, comprising receiving a selection of a first objective and a second objective, the first objective and second objective comprising goals associated with distribution of a plurality of data elements; receiving an indication that the first objective has a higher priority than the second objective; receiving a first goal metric associated with the first objective and a second goal metric associated with the second objective; determining a first forecasted metric based on the first goal metric associated with the first objective; determining a second forecasted metric based on the second goal metric associated with the second objective; and allocating resources for the distribution of a plurality of data elements based on the first goal metric, the second goal metric, the first forecasted metric, the second forecasted metric, and the indication that the first objective has a higher priority than the second objective.

TECHNICAL FIELD

The disclosure generally relates to the field of data elementoptimization. More specifically, the disclosure relates topriority-based optimization of data element usage.

BACKGROUND

Conventional data element optimization is a complex and time consumingprocess, and is often not quantified. For example, producers of onlinevideos and other data elements (e.g., electronic or online ads orcreatives) promoting products and/or services may have a limited budget.Based on limited data, the producers may choose to use one promotionaldata element more often than another, but this decision is often basedon subjective feelings about the merits of the data element's content.

Further, producers of data elements often have a plurality of objectivesand constraints associated with the promotion of products and services.Prioritizing the objectives relative to each other while meeting allconstraints is difficult, if not impossible, in real time.

SUMMARY OF THE DISCLOSURE

Systems and methods are disclosed for optimizing distribution ofresources to data elements, and may comprise receiving a selection of afirst objective and a second objective, the first objective and secondobjective comprising goals associated with distribution of a pluralityof data elements; receiving an indication that the first objective has ahigher priority than the second objective; receiving a first goal metricassociated with the first objective and a second goal metric associatedwith the second objective; determining a first forecasted metric basedon the first goal metric associated with the first objective;determining a second forecasted metric based on the second goal metricassociated with the second objective; and allocating resources for thedistribution of a plurality of data elements based on the first goalmetric, the second goal metric, the first forecasted metric, the secondforecasted metric, and the indication that the first objective has ahigher priority than the second objective.

Systems and methods disclosed herein may further receive a selection ofa first objective by receiving a selection of one of a plurality of keyperformance indicators.

Systems and methods disclosed herein may further allocate resources forthe distribution of the plurality of data elements by determining animpression price associated with publication of one or more of theplurality of data elements, wherein the determined impression pricecorresponds to the degree to which the first goal metric and second goalmetric are to be achieved.

Systems and methods disclosed herein may further receive a selection ofa third objective; receiving a third goal metric associated with thethird objective; determining a third forecasted metric based on thethird goal metric; and reallocating resources for the distribution of aplurality of data elements based on the first goal metric, the secondgoal metric, the first forecasted metric, the second forecasted metric,the third goal metric, the third forecasted metric, and the indicationthat the first objective has a higher priority than the secondobjective.

Systems and methods disclosed herein may further comprise determining atheme associated with the first objective; and based on the determinedtheme, disallowing selection of any key performance indicator as thesecond objective that is not associated with the determined theme.

Systems and methods disclosed herein may further comprise, when thefirst objective corresponds to an objective category associated with aplurality of key performance indicators: receiving a selection of one ofthe plurality of key performance indicators as the first objective; anddisallowing selection of any remaining key performance indicatorsassociated with the objective category as the second objective.

Systems and methods disclosed herein may further comprise determining apriority multiplier based on the indication that the first objective hasa higher priority than the second objective, wherein the prioritymultiplier is based on the degree of higher priority that the firstobjective has over the second objective; and applying the prioritymultiplier when allocating resources for the distribution of a pluralityof data elements.

Systems and methods disclosed herein may further comprise receiving amodification in the selection of the first objective or the first goalmetric; and reallocating resources for the distribution of the pluralityof data elements based on the modification in the selection of the firstobjective or the first goal metric.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a high-level block diagram illustrating a system fordynamically optimizing the use of data elements in accordance withobjectives and constraints.

FIG. 2 is a high-level block diagram illustrating an example of acomputer for use as a server and/or as a client according to techniquespresented herein.

FIG. 3 is a block diagram illustrating an example object hierarchyaccording to techniques presented herein.

FIG. 4 is a block diagram illustrating an example of the grouping ofdata elements into data element groups according to techniques presentedherein.

FIG. 5 is an example user interface displaying data elements and dataelement groups.

FIG. 6 is an example user interface enabling the creation of one or moredata element groups according to techniques presented herein.

FIG. 7 is an example user interface enabling the creation of one or moredata element groups according to techniques presented herein.

FIG. 8 is an example user interface enabling the selection of objectivesassociated with groups of data elements according to techniquespresented herein.

FIG. 9 is an example user interface enabling the creation and/orselection of data elements that may be associated with a data elementgroup.

FIG. 10 is an example user interface enabling the creation and/orselection of data elements that may be associated with a data elementgroup.

FIGS. 11A-11C are example user interfaces for optimizing data elementusage according to techniques presented herein.

FIG. 12 is a flow diagram illustrating an example method for optimizingdata element usage based on multi-touch attribution data according totechniques presented herein.

FIG. 13 is a flow diagram illustrating an example method for optimizingdata element usage based on multi-touch attribution data according totechniques presented herein.

FIG. 14 is an example user interface enabling the selection ofobjectives associated with groups of data elements according totechniques presented herein.

FIG. 15 is an example user interface enabling the selection ofelectronic events associated with groups of data elements according totechniques presented herein.

FIG. 16 is a flow diagram illustrating an example method 1600 forpriority-based optimization of distribution of resources for dataelements, according to techniques presented herein.

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 is a high-level block diagram of a computing environment 100 fordynamically optimizing data elements according to one embodiment. Thecomputing environment 100 may include a publisher server 110, a dataelement server 120 (or “ad server”), a multi-touch attribution (“MTA”)server 125, any number of consumer devices 135, and any number of clientdevices 130 communicatively coupled by a network 190, such as theInternet. In one embodiment, the publisher server 110, the data elementserver 120, and the MTA server 125 may be web servers. In anotherembodiment, the publisher server 110 and/or MTA server 125 may beapplication servers that provide an instance of one or more applications140 to the client device 130. In yet another embodiment, the publisherserver 110, data element server 120 and/or MTA server 125 may providedata to support the execution of the one or more applications 140 on theclient 130. The client device 130 is a computer or other electronicdevice which may be used by one or more users to perform activitieswhich may include browsing web pages on the network 190, or using theone or more applications 140. The client device 130, for example, may bea personal computer, personal digital assistant (PDA), or a mobiletelephone. Only one publisher server 110, one data element server 120,one MTA server 125, and one client device 130 are shown in FIG. 1 inorder to simplify and clarify the description. Other embodiments of thecomputing environment 100 may include any number of publisher servers110, data element servers 120, MTA servers 125, and/or client devices130 connected to the network 190. Further, while the publisher server110 and data element server 120 are depicted as separate in the exampleof FIG. 1, the features of the publisher server 110, data element server120, and MTA server 125 may be integrated into a single device on thenetwork 190. The MTA server 125 may provide multi-touch attributionservices, as will be discussed further herein.

The network 190 represents the communication pathways between (e.g.,communicatively coupled) the publisher server 110, data element server120, MTA server 125, and client device 130. In one embodiment, thenetwork 190 is the Internet. The network 190 may also include dedicatedor private communications links that are not necessarily a part of theInternet. In one embodiment, the network 190 uses various communicationstechnologies and/or protocols. Thus, the network 190 may include linksusing technologies such as Ethernet, 802.11, integrated services digitalnetwork (ISDN), digital subscriber line (DSL), asynchronous transfermode (ATM), etc. Similarly, the networking protocols used on the network190 may include the transmission control protocol/Internet protocol(TCP/IP), the hypertext transport protocol (HTTP), the simple mailtransfer protocol (SMTP), the file transfer protocol (FTP), etc. Thedata exchanged over the network 190 can be represented usingtechnologies and/or formats including the hypertext markup language(HTML), the extensible markup language (XML), etc. In addition, all orsome of links may be encrypted using encryption technologies such as thesecure sockets layer (SSL), transport layer security (TLS), secure HTTP(HTTPS), and/or virtual private networks (VPNs). In another embodiment,the entities may use custom and/or dedicated data communicationstechnologies instead of, or in addition to, the ones described above.

As shown in FIG. 1, client device 130 may execute an application 140,such as a web application or browser, that allows a user to retrieve andview content stored on other computers or servers on the network 190.The application 140 may also allow the user to submit information toother computers on the network 190, such as through user interfaces 150,web pages, application program interfaces (APIs), and/or other dataportals. In one embodiment, the application 140 is a web browser, suchas MICROSOFT INTERNET EXPLORER or MOZILLA FIREFOX. The application 140may support technologies including JavaScript, ActionScript, and otherscripting languages that allow the client device 130 to perform actionsin response to scripts and other data sent to the application via thenetwork 190. The application 140, as further discussed herein, may alsoutilize data and/or other services from MTA server 125. In someembodiments, functions ascribed herein to the application 140 areimplemented via plug-ins such as ADOBE FLASH. In some embodiments, theapplication 140 may present a demand-side platform (“DSP”) to users(which may be authorized users), which enables would-be advertisers oragents thereof to purchase ad space.

Any number of consumer devices 135 may also connect to the network 190,which may enable consumers of network content to view data elements suchas advertisements distributed using the application 140. While theclient device 130 is depicted as having a data element player 160, videoplayer 170, and data element script 180, these entities and more may bepresent on any or all of the consumer devices 135. In addition, many ofthe attributes and behavior of a client device 130 may be also presentor implemented on the consumer device 135.

The publisher server 110 may deliver data associated with a userinterface 150, such as a web page, to the application 140 over thenetwork 190. The publisher server 110 may also communicate with MTAserver 125, and act as a relay for information between the application140 and the MTA server 125, including information which may be utilizedwhen rendering a user interface 150. The application 140 may then loadthe user interface 150 and present it to the user. User interface 150may correspond to any of the user interfaces discussed herein, and anyof the user interfaces which may be displayed by application 140. Theuser interface 150 may include a video player 170 for presenting onlinevideos and a data element player 160 which may present electronicadvertisements and/or other promotional materials to the user of clientdevice 130 and/or consumer using the consumer device 135. The dataelement player 160 may be used to display any of the data elementsdiscussed herein to a user. The video player 170 can be any video playersuitable for online video such as WINDOWS MEDIA PLAYER, REALPLAYER,QUICKTIME, WINAMP, or any number of custom video players built to run ona suitable platform such as the Adobe Flash platform.

The data element player 160 may comprise JavaScript, ActionScript and/orother code executable by the application 140 that may be delivered tothe client device 130 in addition to or as part of the user interface150. A data element script 180 may contain code readable and/ortransformable by the data element player 160 into operationalinstructions that govern behavior of the data element player 160. Theapplication may execute the data element player 160 natively, directly(e.g., as JavaScript) or via a browser plug-in module (e.g., as a Flashplug-in). The data element player 160 may communicate with the dataelement server 120 over the network 190 to request and receive contentfor presentation on the client device 130. A data element may compriseany computer-executable code (e.g., JavaScript, ActionScript, Flash, orHTML) whose execution may result in the presentation of text, images,and/or sounds to the user. The text, images, and/or sounds may promoteone or more products, services, viewpoints and/or actions. A dataelement can be a linear data element (i.e., promotional content thatinterrupts the presentation of a video) or a non-linear data element(i.e., promotional content that is presented concurrently with a video)presented either before, during, or after the video. A data element canalso be textual, graphical (such as a banner promotion), or a videopromotion. A data element can be presented as overlaying the onlinevideo or in any other position within the user interface 150. A dataelement can also be interactive and, in one embodiment, a data elementcan transition from one of the aforementioned varieties of promotionaldata elements to a different variety or trigger an additional dataelement in response to an action by the user of client 130 or consumerusing a consumer device 135.

The MTA server 125 may provide multi-touch attribution data and/orfunctionality to the one or more applications 140. Consumer decisionsare often complex, and cannot be attributed to a single source. If aconsumer purchases a particular model of a car, for example, thatdecision may be the result of many factors. There may have been manymagazine articles, web advertisements, video reviews, and other factors(or “touches”) that led to that decision. Multi-touch attribution, asdisclosed herein, allows for tracking and analysis of these factors,which allows for a more granular understanding of consumer behavior, andreturn on investment (“ROI”) determinations that were not possible priorto the Internet.

Software code may be associated with or embedded in advertisements,articles, videos, audio, or any form of consumer multimedia. The codemay collect multi-touch attribution (“MTA”) data on the consumers views,mouse clicks, searches, listening preferences, and/or other behaviors,and make it available to an MTA server 125, or other devices. This MTAdata may be used for analyzing the behavior of the users of electronicdevices, and may further be used to analyze consumer behavior in orderto optimize advertising. Multi-touch analysis may be performed, whetheron the MTA server or elsewhere, using any number of algorithms which mayapply weights to each touch. The weights may be applied in any number ofways. For example, all consumer exposures to car-related media may beweighted equally. Exposures further back in time may be weighted less,first exposures may be weighted more, proactive exposures may beweighted more (e.g., the consumer searches for the car in a browser),and/or concentrated exposures in the exposure timeline may be weightedmore, etc. Using the MTA data and any applied weights, an ROI may becalculated, which may be based on costs, such as in advertising costs,and the conversion data. Using historical MTA data, ROI may be forecast,for example by the MTA server 125, publisher server 110, and/or clientdevice 130, as will be discussed further herein.

FIG. 2 is a high-level block diagram illustrating on example of acomputer 200 for use as a client device 130, consumer device 135, and/oras a server, such as a publisher server 110, a data element server 120,or an MTA server 125. Illustrated are at least one processor 202 coupledto a chipset 204. The chipset 204 may include a memory controller hub220 and/or an input/output (I/O) controller hub 222. A memory 206 and agraphics adapter 212 may be coupled to the memory controller hub 220,and a display 218 is coupled to the graphics adapter 212. A storagedevice 208, keyboard 210, pointing device 214, and network adapter 216may be coupled to the I/O controller hub 222. Other embodiments of thecomputer 200 have different architectures. For example, the memory 206may be directly coupled to the processor 202 in some embodiments.

The computer 200 may be adapted to execute computer program modules forproviding the functionality described herein. As used herein, the term“module” refers to computer program logic configured and used to providethe specified functionality. Thus, a module can be implemented inhardware, firmware, and/or software. In one embodiment, program modulesare stored on the storage device 208, loaded into the memory 206, andexecuted by the processor 202. The storage device 208 is acomputer-readable storage medium such as a hard drive, compact diskread-only memory (CD-ROM), DVD, or a solid-state memory device. Thememory 206 is also a computer-readable storage medium and storescomputer-executable instructions and data used by the processor 202.

In one embodiment, the memory 206 stores computer-executableinstructions that cause the processor 202 to implement a method fordisplaying data elements. The computer-executable instructions stored bythe memory comprise instructions for the application 140. In oneembodiment, after delivery of the user interface 150 and data elementscript 180 to the client device 130 by the publisher server 110, thecomputer-executable instructions stored by the memory 206 furthercomprise instructions for the user interface 150, the data elementplayer 160, the video player 170, and the data element script 180 asshown in FIG. 2.

The pointing device 214 may be a mouse, track ball, touch screen, orother type of pointing device, and is used in combination with thekeyboard 210 to input data into the computer system 200. The graphicsadapter 212 displays images and other information on the display 218.The network adapter 216 couples the computer system 200 to the network190. Some embodiments of the computer 200 have different and/or othercomponents than those shown in FIG. 2.

The types of computers 200 used by the entities of FIG. 1 can varydepending upon the embodiment and the processing power required by theentity. For example, a client device 130 that is a mobile telephonetypically has limited processing power, a small display 218, and mightlack a pointing device 214. A server providing a data element server120, in contrast, might comprise multiple servers working together toprovide the functionality described herein. Also, a server typicallylacks hardware such as the graphics adapter 212, the display 218, anduser input devices.

Some portions of the above description describe embodiments in terms ofalgorithms and symbolic representations of operations on information.For example, the description corresponding to FIGS. 2-12 relate totechniques that optimize data element usage. These algorithmicdescriptions and representations are commonly used by those skilled inthe data processing arts to convey the substance of their workeffectively to others skilled in the art. These operations, whiledescribed functionally, computationally, or logically, are understood tobe implemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to depict to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment. Software and firmware configurations of the modules andcorresponding instructions described above can be stored in, forexample, the storage device 208 and/or the memory 206 and can beexecuted by, for example, the processor 202, adapters 212, 216,controllers 220, 222, and/or multiple such devices operating inparallel.

FIG. 3 illustrates a block diagram of an object hierarchy according totechniques presented herein. In one technique, an owner 305, who may bea creator and/or producer of data elements, such as promotional dataelements, and may be a buyer of promotional space, may manage orotherwise own one or more campaigns 310. In general herein, a user ofthe application 140 and/or client device 130 will be referred to as a“user,” though the user may or may not be the owner 305 (the user maybe, for example, an advertiser running campaign via client 130). Acampaign 310 may be a collection of one or more data elements 315 thatshare a common idea or theme. As discussed above, a data element 315 maycomprise any computer-executable code whose execution may result in thepresentation of text, images, and/or sounds to the user. Each dataelement 315 may further comprise one or more creatives 320, each ofwhich may correspond to at least a portion of the text, images, and/orsounds presented to the user. Finally, each creative 320 may furthercomprise one or more media files 325, such as textual, image, and/oraudio files.

In another technique, data elements may be organized into one or moredata element groups 330. The data element group 330 may enable users tomore effectively plan and optimize the meeting of constraints relatingto data elements that share common objectives and/or budgets. Forexample, a user may have a business objective of programming allocatingresources, such as a budget, across promotional data elements in themost cost-effective manner. The data element group 330 object may allowthe owner to organize groups of data elements 315 that share one or morecommon objectives. While one campaign may be associated with a pluralityof data element groups, the application may enforce a rule that dataelement groups cannot be shared across multiple campaigns. Further, theapplication may require that at least one data element 315 be associatedwith a data element group 330. While data elements 315 may be associatedwith a data element group 330, the application may allow data elements315 to remain unassociated with a data element group 330.

As will be shown, techniques discussed herein may allow an owner 305 tomonitor data element group 330 performance via one or more userinterfaces 150. Owners 305 may be able to forecast supply, pricing, andperformance associated with data elements 315 and data element groups330. Owners may also be able to programmatically optimize budgetallocation across data elements that perform the best according to oneor more objectives and/or one or more constraints. Owners may further beable to manually optimize allocations of resources, and may allocatemore resources, such as a budget, to better performing data elements.Owners may also be able to run reports against data element groupperformance, further enabling the selection of the most effective dataelements for reaching a given set of objectives and constraints.

FIG. 4 is a block diagram illustrating an example of the grouping ofdata elements into data element groups according to techniques presentedherein. As discussed above, data elements may be used as promotionalcontent, and may be associated with a brand 405. The medium for thepresentation of promotional content may be video 410, although othermediums would be consistent with techniques presented herein. A givenbrand 405 may have a one or more associated campaigns 310. Each campaign310 may, in turn, have any number of data element groups 330 and anynumber of data elements 315 associated therewith. Constraints may be setat the campaign level, for example, for resource constraints, such as abudget. These constraints may bind objects lower on the objecthierarchy, such as individual data element groups 330 and data elements315. Similarly, constraints set at the data element group level 330 maybind data elements 330 associated with the data element groups 330.

Data element groups 330 may be created by an owner 305, or some otheradministrator or sufficiently privileged user. Alternatively, dataelements may have associated tags and/or properties, and data elements315 may be automatically grouped into data element groups 330 based uponthese tags and/or properties. For example, data elements 315 sharingsimilar optimization objectives and/or maximum effective cost perthousand impressions (“eCPM”) may be grouped under one data elementgroup 330. Forecasts of delivery and/or key performance indicators(“KPIs”) may be determined and/or viewed at the data element group 330level as well at the level of individual data elements within the dataelement group. Budgets may be automatically allocated across multipledata elements 315 within a given data element group 330.

One or more users 415, which may correspond to one or more owners 305,may access the application in order to create and associate data elementgroups 330 and data elements 315. The data element groups 330 and dataelements 315 may utilize one or more creatives 420, which may be storedin a data store, such as storage device 208. Data element groups 330and/or data elements 315 may further be utilized in relation to one ormore sites 425, such as websites, and segments 430. Sites 425 may beused to target certain topics, for example sports and/or entertainment.Segments 430 may be target audience objects. For example, a data segment430 object may represent females 18 and over. Data element groups 330and/or data elements 315 may also be utilized in relation to geographicareas 435. For example, a data element group may contain onlypromotional data elements that are to be run in a particular geographicregion.

As discussed herein, objectives and/or constraints may be optimized inorder to meet or exceed the one or more objectives within anyconstraints. For example, objectives may be to hit a target eCPM for agiven budget, to maximize target impressions, to maximize the completionrate, and/or to maximize the click-through rate. Any number of otherobjectives/goals/KPIs may be used. Optimization, for example of abudget, may occur in multiple ways. Optimization may occur across videochannels, for example by optimizing how much of a budget to apportion toeach video channel given an assessment of the effectiveness of thechannel towards meeting the one or more objectives. Optimization mayalso occur across multiple campaigns 310, a campaign being a collectionof related data elements 315 and/or data element groups 330. Onecampaign may be determined to be more effective than another campaign ina certain medium or in promoting a certain brand, and resources may bebalanced accordingly. Optimization may further occur across data elementgroups 330 within a given campaign. Some data element groups may proveto be more effective at meeting objectives and constraints than others,and resources may be continually rebalanced accordingly. Thisoptimization may include data elements that are not affiliated with dataelement groups. Optimization may further occur across data elements,such as the data elements within a given data element group. In thismanner, individual data elements that perform better for a given set ofobjectives and/or constraints may be given more resources or otherwiseutilized more frequently. Further, optimization may be applied at thelevel of creatives, which may comprise a portion or version of a givendata element. Optimization may also occur when selecting an optimalprice of a bid, if, for example, bidding for space to promote dataelements. Thus, optimization may occur at one or many levels of theobject hierarchies shown in FIG. 3.

FIG. 5 is an example campaign user interface 500 displaying dataelements and data element groups. The campaign user interface 500 maydisplay any campaigns 505 to which the application user has access toview, such as any and all campaigns associated with a given owner 305.The campaign user interface 500 may also display any data element groupsand/or independent data elements associated with each campaign. Eachcampaign 505 may have associated data element groups listed beneath,which may themselves have associated data elements listed beneath. Thedata element groups, data elements, and campaign listings may beexpanded by default, contracted by default, or some items may beexpanded or contracted, according to user preference. For example, auser may select that campaigns 505 are expanded, showing all associateddata element groups associated therewith, but the data element groupsthemselves may be contracted by default, such that the user cannotimmediately see the data elements associated with each data elementgroup.

The campaign user interface 500 may further display fields associatedwith each campaign, such as the campaign's name 510, active/inactivestatus 515, start and end dates 520, pacing 525, impressions 530 (numberof times a data element is presented to a member of the targetaudience), amount spend 535 thus far, bid 540 (which may comprise theaverage bid for impressions of a promotional data element, or perthousand impressions, etc.), click rate 545, targeting 550, and otheroptions 555.

Pacing 525 may indicate a rate at which data elements are being madeavailable relative to a goal. For example, in advertising, if a dataelement has a spend goal of $10 over a 10 second period, pacing is 100%if $10 is actually spent delivering the data element to an audienceduring that time. Pacing would be 200% if $20 was spent delivering thedata element during that time period, and so on.

Targeting may be a filtering mechanism to make sure that promotionaldata elements run against a qualified pool of inventory and audience.For example, certain devices (e.g., only tablet computers), platforms(e.g. only Android), topics (e.g., only sites that over index forsports), audience segments (e.g., only females 18 and up), time periods(e.g., only evenings), may be specifically targeted. Techniquesdiscussed herein may attempt to optimize for targeting to find the mostvaluable impressions for the cost.

A user of application 140 might create a new data element group in thecampaign user interface 500 in a number of ways. For example, the usermight select “create new” 510 to create a new data element group forassociation with a certain campaign. A user may also be able to clone anexisting data element group, which may clone the data element group andany associated data elements. A user may also be able to create ordelete data elements within a data element group, or data elements thatare unaffiliated with a data element group.

FIG. 6 is an example user interface 600 allowing the creation of one ormore data element groups according to techniques presented herein. Whena user selects to create a new data element group, user interface 600may be displayed, where a user may be able to select general settings,objectives, and/or constraints for the data element group. Theseselections may automatically propagate to any data elements associatedwith the data element group. Settings selected in the data element groupuser interface 600 may be reflected in the campaign user interface 500.

The user may also select a status 515 of the data element group, whichmay reflect whether any data elements in the data element group may beused in the real world, such as for advertising. The data element groupmay be “paused” by default, such that ad space associated with the dataelements of the data element group would not be obtained. If the dataelement group is set to “live,” a user may still be able to individuallypause data elements associated with the data element group.

The user may further select a data element group name 510, and maydesignate any notes or comments 605 about the data element group. Theuser may also select one or more start and end dates 607 of the dataelement group, which may represent dates promotional data elements mayrun in one or more forms of media. Additional start and end dates may bedesignated, such as by selecting a flight option 610. As used herein, aperiod between a start and end date may be known as a “flight.”

The user may also designate a data element group goal 615, which mayindicate a total budget (spend total) for the data element group. Theuser may also be able to switch auto-allocation 620 on and off, whichenables automatic optimization of data elements associated with the dataelement group, as will be further described herein. The objectives maybe optimized across data elements of the data element group based on“mid-flight” metrics or other performance data. Once a minimum level ofperformance data is obtained, usage of a given data element and budgetassociated therewith may be refined at regular time intervals, such asdaily. If the user turns off automatic allocation 620, the user maystill be able to manually allocate data element goals for data elements,for example in the data elements tab.

Goals or objectives set at the data element group level may be set toadhere to goals set at higher object levels, such as at the campaignlevel. For example, start and end date ranges 607 may be prohibited fromgoing outside of any start or end date ranges set at the campaign level.The spend total may be set to be equal to or less than the spend totalset at the campaign level, if set.

As an example, a user may set automatic budget allocation 620 across oneor more data element groups and independent data elements, and automaticperformance optimization for data element usage within a data elementgroup. Optimization techniques, such as linear programming, may be usedto optimize for multiple goals while staying within any number ofconstraints. The optimization may occur recursively up or down throughthe object hierarchy. A first constraint set at the campaign level maybe a spend goal of $2.5 million. The start and end dates may be set asthe month of October, 2014. Based on one or more campaign-level or otherconstraints, data element groups may be automatically or manuallycreated corresponding to one or more audience segments.

The example campaign may contain any number of data element groups. Afirst data element group may correspond to would-be buyers of a certainautomotive brand. A user may set delivery constraints at the dataelement group level. For example, the user may set the spend goal forthe data element group to be $1 million, and the maximum eCPM to be $15.The user may also set objectives or goals, for example a target audienceof females aged 18-49, with a click-through rate goal of 2%.

A second data element group may correspond, for example, to dataelements of an automotive marketer. A user may set delivery constraintsat the data element group level. For example, the user may set the spendgoal for the data element group to be $500,000, and the maximum eCPM tobe $10. The user may also set objectives or goals, for example a targetaudience may be set to adults 18 and over, and a completion rate goalmay be set to 85%. As discussed above, individual data elements may beassociated with a campaign regardless of whether they are associatedwith a data element group. For example, the user may create anindividual promotional data element with a spend goal of $500, themaximum eCPM of $12, and with an objective of minimizing cost perthousand impressions (CPM). Further, as discussed above, while thecampaign may be automatically optimized, a user may manually set budgetsof unaffiliated data elements, one or more data element groups and/orindividual data elements within groups.

FIG. 7 is an example user interface 700 illustrating further usersettings that may be configured when creating a data element group. Theuser may designate other objectives and/or constraints in addition tobudget. For example, the user may designate impression targets or grossdata element revenue targets to be automatically optimized. As arestrictive goal, the user may also designate a cap for the frequency705 that a data element may be used in a campaign. Frequency capping maybe managed at the data element group level, and may override frequencycapping at the data element level. As noted previously, frequencycapping may conform to the frequency capping setting from the campaignobject.

Additional constraints may also be set by the user when creating acampaign, data element, or, in the example shown in FIG. 7, a dataelement group. The user may further set a cost per thousand impressionsprice cap 710. A buyer margin 715, for example a percentage of mediacosts and/or vendor fees, and a pass-through cost 720, such as a costper thousand constraint, may also be designated. Pass-through costs 720may include other costs and third party fees which may not be otherwisedirectly logged in application 140. Owners 305 may wish to includepass-through costs to ensure that the media cost numbers, which may bedefined as the maximum bid minus any pass-through costs, are realistic.For example, if the maximum bid is $10, but there are $3 in expecteddata costs, up to $7 is left to be allocated for the media cost. If anyof the billing fields are set, they may bind any data elementsassociated with the data element group. As a result, changing billingfields in the settings of associated data elements may be disabled.

FIG. 8 further illustrates the selection of objectives associated withthe creation of a data element group according to techniques presentedherein. Selections on the user interface 800 may be automaticallyapplied to one or more data elements associated with the data elementgroup. As discussed above, data element group objectives may beoptimized by allocating resources based upon performance of dataelements at some predetermined time period after the start of thecampaign (i.e. “mid-flight”). The performance of one or more associateddata elements may further be determined given any objectives and/orconstraints defined in the user interface 800.

The user may select whether optimization 805 for the data element groupis set to on, off, or manual. If the data elements are being used foradvertising, selecting “on” may mean that the application willautomatically deliver impressions based on a real-time marketplace foreach data element at the lowest possible price to meet the requiredobjectives of the data element group. Optimization 805 may be turned onby default. In order enable manual or turn off optimization 805 at thedata element group level, the user may be required to turn off “AutoAllocation Across Data Elements” in user interface 600. The optimization805 setting may automatically propagate to all data elements associatedwith the data element group.

The user may also select a target eCPM 810, which, as indicated by theasterisk, may be a mandatory field. As discussed above, the eCPM 810 isthe effective cost per thousand impressions, and may be calculated bydividing total earning for data elements in the data element group bytotal number of impressions of data elements in the data element groupin thousands. Associated data elements or “child data elements” may beunable to use a different target eCPM when data element groupoptimization 805 is turned on. The target eCPM 810 may be enforced as arestrictive ceiling. If automatic goal allocation 620 is turned off,child data elements may be able to have a different target eCPM as longas they are at or below the target eCPM goal. If automatic goalallocation 620 is turned on, the optimizer may determine the best way toallocate the budget (impression, spend or gross revenue) across anychild data elements. Once enabled, a user may be restricted from editingthe delivery goal, target eCPM, and/or objectives at the individual dataelement level.

Multiple objectives 815 may be selected by a user and ranked in theselections list 820. For example, a user may designate a primaryobjective, a secondary objective, a tertiary objective, etc. The dataelement group optimization algorithm may take the ranking into accountwhen optimizing allocation of resources across a given set of dataelements, given the assigned objectives and constraints. For example,objectives may be assigned varying weights that affect how optimizationis performed. Positive factoring may be given to higher priorityobjectives such that the application 130 may be more likely to bid forspace which meets a higher priority objective (within the maximum CPMgoal or other constraints), rather than a lower priority objective.

The importance of each objective in a list or hierarchy of objectivesmay be reflected by the allocated bidding price. The dedicated biddingprice allocated for each objective may be a weighted portion of themaximum CPM (or other budgetary constraint), while the weight of eachobjective may correspond to the priority level (i.e., higher priorityobjectives may be given higher weights). The assigned budget may also beadjusted by the achievement difficulty and rareness of a givenobjective.

For example, a user may designate a list of objectives 820. A primaryobjective may be in-target impressions of females 18 and up, a secondaryobjective may be a completion rate of at least 80%, and a tertiaryobjective may be a CTR of at least 1%. The total target eCPM 810 may be$15. Initially, a largest portion, for example 50%, of the eCPM may beautomatically allocated to the primary objective, in this case thein-target objective. A second largest portion, such as 33%, may beallocated to the secondary objective. And a smallest portion, such as17%, may be allocated to the tertiary objective. The bids may then beautomatically adjusted by rareness. For example, the average in-targetrate may be 25%, so in-target impressions may receive an eCPM of $30.Completed impressions may receive an eCPM of $6.25 (assuming an averagecompletion rate of 80%), and CTR may receive $250 eCPM (assuming anaverage CTR of 1%).

After the campaign begins, in mid-flight the allocations may be adjustedby achievement difficulty. For example, if the secondary objectiveaverage completion rate of 80% is achieved, allocations may be increasedto the primary and/or tertiary objectives to increase the likelihoodsthat they will also be met. In other words, allocations for objectiveswith a low achievement difficulty may be reduced relative to otherobjectives with a higher achievement difficulty. This behavior is notnecessarily binary. Rather, as a given objective becomes closer to beingmet, the allocation may be correspondingly reduced.

FIG. 9 is an example user interface 900 further allowing the creation,modification, and/or selection of data elements and attributes that maybe associated with the creation of a data element group. The userinterface 900 may list data elements associated with the data elementgroup 905. Each data element may have an associated status indicator910, a goal allocation 915, a start date and end date of use 920, forexample if the data elements are advertisements in a campaign, a pacingpercentage 925, which determines how promotional data elements arespaced out in time, a number of impressions 930, the spend total 935,the revenue generated 940, the associated bid 945, the click rate 950,the targeting 955, and other options 960 and 965. The user interface900, along with other user interfaces presented herein, may display aforecasted delivery, such as a supply curve for eCPM. The audience for aselected data element group or data element may also be forecasted 975.

Several of these fields will now be discussed in greater detail. Thegoal allocation field 915 may determine how much of an available budgetis intended to be spent on data elements in the data element group. Ifthe user has selected automatic allocation 620, the default may be setto auto in the goal allocation field 915. The user may be able to setthe goal allocation field 915 to manual, which may in turn switch theselection in the automatic allocation field 620 in a correspondingmanner. The percentages of the goal allocation field 915 may be requiredto total 100%. The allocation percentages may be not editable ifautomatic allocation 620 is enabled, as the percentages would be decidedby the optimization features of the application. If a budget has beenset at the data element group level, then the budgets of all of the dataelements within the data elements may be set to remain equal to or belowthe data element group total.

If, on the other hand, the goal allocation field 915 is set to manual, auser may be able to adjust the percentages of each data element. As adefault, a manual setting may result in the first data element receivinga 100% allocation, which may then be modified by the user. The goalestimate, which may be a dollar amount under the goal allocation field915, may be not editable by the user, and may be derived only from thepercentage designated and the budget of the data element group. The sumof the allocation percentages may be set to total 100%. A user may alsobe able to allow a portion of the budget to be automatically allocated,and a portion to be manually allocated.

Status indicators 910 may be placed near goal allocation percentages.The status indicators 910 may be colors or symbols, such as green orred, up or down arrows, etc. The status indicators 910 may have a firststatus if the allocation is increasing (an indication that the dataelement has been determined to be more effective at reaching objectivesthan in previous optimization cycles), and another status if theallocation is decreasing (an indication that the data element has beendetermined to be less effective at reaching objectives than in previousoptimization cycles). A third status may also exist if the allocationhas not changed in the recent allocation cycle from a prior allocationpercentage. For example, if a data element allocation is increasing, theassociated status indicator may be green. If the allocation isdecreasing, it may be red, and if the allocation is the same or within apredetermined distance from the same allocation, the status indicatormay be gray.

A user of the application may be able to add new data elements in one ormore ways, such as by clicking a button 965 on the user interface. Auser may further be able to edit the settings of each data element,clone a data element (which may be automatically associated with thedata element group), and/or delete data elements.

One or more user interfaces presented herein may also display a forecastwindow 968. A user may be able to toggle between different forecastviews. A default view may be an aggregated data element group forecast.An aggregated data element group forecast is a summarized view of allchild data elements. Alternatively or in addition, the application mayalso be able to display a forecast window for individual data elements.In response to a user clicking on an individual data element, theforecast window for the individual data element may be displayed.Similarly, clicking on a data element group may cause the aggregateddata element group forecast to be displayed.

The application may also create reports about data element groups anddata elements for display. Report keys may comprise one or more of: adata element group, data element group end date, data element group goalor objective, data element goal or objective type, data element groupidentifier, data element group pricing type, data element group startdate, and a data element group type. Similar reports may be generatedfor individual data elements, for campaigns, or for a group ofassociated campaigns.

Data element group optimization will now be further discussed. At a highlevel, data element group optimization may involve a user inputting astrategy. Accordingly, objectives and/or constraints such as flight,budget, performance goals and associated data elements may be defined.The data elements may have a common KPI goal, which will guide theapplication to allocate the budget accordingly. Once the data elementgroup has been created, promotional data elements may be run, andassociated performance data may be collected, for example on a certaintime interval such as on a daily basis. The budget may then be assignedacross data elements based on the performance data and any constraintsor restrictions (e.g. a minimum spend notwithstanding goal). Theperformance data may allow a determination of which data elements arebetter meeting the one or more objectives, such as, for example, byattracting higher click rates and/or click-through rates. The budget maybe assigned for a subsequent time interval, such as for a day, and itmay be reevaluated at each time interval. Alternatively, optimizationmay be performed in real time. Each data element may be given a trainingbudget such that each data element may have the chance to prove itselfeffective, for example in an advertising scenario. A statisticallysignificant amount of performance data for a given data element may berequired to be collected before a data element's budget may be reducedbelow a predetermined threshold, such as being reduced to zero. Dataelement groups may be required to make a first budget allocation to eachdata element in the group within a predetermined time period, such aswithin 48 hours of the data element group flight. This process mayiteratively loop each time interval until the campaign ends. Over time,as performance data accumulates, the application may become moreaggressive in assigning resources such as budget to data elements thatprove to be more effective.

For data elements with in-target goals, where there is no feedback loop,it may be assumed that if a data element is using an online campaignratings (OCR) application, a feedback loop may be used to assess actualperformance. If a data element is not using an OCR application, it maybe assumed that the optimization estimates were delivered.

Possible features of the data element group optimization algorithm willnow be discussed in greater detail. The algorithm may determine theselected goals of the data element group, for example budget desired,cost per thousand cap, KPI goals, flight length, etc. After the dataelement group has run for a period of time, such as a predetermined timeinterval, the achieved goals of the data element group may bedetermined, such as budget delivered, money spent, KPIs delivered, etc.The data element group plan for the next time interval, such as apredetermined time interval cycle, may then be determined. Based on thebudget delivered, the minimal training size of the data element groupmay be determined. Based on the average KPI (supply), the KPI goals(demand), and the indicated priorities, the value of each KPI may beevaluated (the same as controller optimization) as a data element groupbuying plan. The achieved KPI of each data element in the data elementgroup may then be read, for example, in terms of click counts,completion counts, conversion counts, etc. Achieved KPI performance datamay be converted to click rate, completion rate and conversion rate,etc. Based on the achieved KPI of each data element and the determinedvalue of each KPI, the value of each data element towards the dataelement group may be evaluated. The value may be determined as, forexample, by multiplying the KPI of the data element by the buying plan(the buying plan may be a representation of the importance of eachobjective) of the data element group. The forecasted KPI of each dataelement in the next cycle may then be read. Based on a received ordetermined forecasted KPI, the opportunity risk of the value of the dataelement dropping may be modeled. The forecasted supply of each dataelement in the next cycle may then be received or determined. The budgetfor each data element in the data element group may then be optimized,for example by linear programming. A feasibility region within a rangeof constraints may be determined, and the optimum distribution ofresources may further be determined in part based upon which point inthe feasibility region most effectively meets the objectives. Inparticular, intersection points of constraint lines along edges of thefeasibility region may be evaluated. The optimization may be determined,for example, using the following technique:

MAX sum(DEperform*DEbudget)

S.T sum(DEbudget)=DEGgoal

0<=sum(DEbudget*eCPM)<=DEGspend_goal

BOUNDS: DEmingoal<=DEbudget<=DEmaxsupply

The objective of these equations may be to maximize the aggregatedperformance at the data element group level. The DEbudget may representthe budget allocated to each data element in a data element group, whileDEperform may represent the performance of each data element. Theconstraints may be data element group level budget (DEGgoal andDEGspend_goal), and data element level supply (DEmaxsupply) and a dataelement configuration goal minimum (DEmingoal). The solution may then beprovided to the data element level optimizer.

FIG. 10 is an example user interface 1000 configured for enablingcreating and/or editing data elements that may be associated with a dataelement group. After selecting to create a data element in theapplication 140, such as one associated with a data element group, theuser may be shown user interface 1000. The status 1005 of the dataelement may be shown as either paused or live. If a data element ischanged from live to paused mid-flight (during a campaign), a warningmessage may be displayed, and subsequent action by the applicationtaken, that automatic allocation will be set to 0% in the data elementgroup user interface 900 and elsewhere. The budget may then bere-optimized for any remaining live data elements in the data elementgroup, and/or for any data elements left in the campaign generally. Auser may be prohibited from changing the status from live to pausedunless he or she has also set the manual goal allocation to 0% in thedata element group user interface 900.

The data element name 1010 may be designated by the user. The dataelement group 1015 associated with the data element may also beindicated. As discussed previously, the data element may inherit pricingand optimization criteria, as well as any other restrictions orconstraints set at the data element group level.

A start date and end date 1020 (flight), in the case of published orpromotional data elements, may also be displayed and input by the user.Flight dates of individual data elements may be prohibited from fallingoutside of the one or more flight dates of the parent data elementgroup.

Objectives or goals 1025 may also be designated at the data elementlevel. However, if the data element group optimization is activated,this section may be deactivated. A message may be provided to the userindicating that, to enable data element goal selection, the autoallocation across data elements 620 should be turned off. The dataelement group optimization algorithm may automatically allocate a subsetof the data element group.

If the automatic allocation across data elements 620 is set to on, theremay be no specific goals that can be added for the data element.However, the user may be able to determine a minimum level allocationper data element. For example, the user may set the minimum number ofimpressions that must be provided for the data element, in the case ofadvertising. The minimum level (along with the combined minimum levelsset by other data elements) may be prohibited from exceeding the valueset at the data element group level. The minimum goal may be allocatedto the data element even if the data element is underperforming relativeto other data elements on the selected optimization objectives. Even ifa minimum goal is selected, the application optimizer may stilldesignate a higher number if the data element outperforms other dataelements in the data element group based on, for example, mid-flightperformance.

FIG. 11A is an example user interface 1100 displaying a data element bidand optimization page, which may be associated with the creation and/orediting of data element metadata, according to techniques presentedherein. If the associated data element is part of a data element groupwith optimization 1105 turned on, the user interface 1100 may benon-editable. The user may not be able to change the target eCPM 1110 orany of the objectives 1115. If optimization 1105 is turned on andautomatic allocation across data elements 620 is turned on, the user maybe enabled to edit the objective 1115 and/or objectives panel 1117.Further, an “objective centric workflow” flag may be enabled to allowthe user to select objectives for the objective panel 1117. If, however,automatic goal allocation is set to manual, or turned off, the user maybe able to edit the target eCPM and/or objectives 1115. As a result,each data element within a data element group and campaign may be ableto have different optimization goals.

As discussed above, optimization may be performed across data elementgroups 330, as well as across particular data elements 315, regardlessof whether they are affiliated with a data element group. The objectivesand/or constraints of the data element may be determined, for example,in terms of budget desired, cost per thousand impressions cap, KPIgoals, flight length, etc. The achieved goals and/or constraints of thedata element may also be determined, for example, in terms of budgetdelivered, money spent, KPI delivered, etc. The data element budget,cost per thousand impressions cap, etc., may then be determined for thenext cycle, where the cycle may be a predetermined period of time. Basedon the pacing of the budget goal, e.g., the rate at which the budget isbeing spent, the base value may be adjusted. Adjusting the base valuemay help keep pacing at or near 100%. The base value may represent theimportance of the pacing objective. Pacing may automatically be given anon-configurable top priority, so the system may calculate a base valuefirst. The importance of each KPI may then be determined based on theaverage KPI (supply, the KPI goal (demand), and/or any user-indicatedpriorities. The optimization algorithm may then attempt to maximize theone or more KPIs with respect to cost per thousand impressions.

FIG. 11B is an example user interface 1170 displaying a user interfacepage which may be presented to the user when optimization 1105 is turnedoff, or turned to manual. The user may be enabled to designate the MediaBid 1120 manually, which may set the cost per thousand (“CPM”)impressions bid price. The user may also be able to designate the DirectInventory Ad Priority 1125. Other embodiments presented herein mayoptimize for marketplace bid opportunity, rather than Direct InventoryAd Priority 1125.

FIG. 11C is an example user interface 1180 displaying a data element bidand optimization page, which may be associated with the creation ofobjectives associated with the distribution of data elements, accordingto techniques presented herein. User interface 1180 is an example ofwhat may be displayed when optimization 1105 and automatic allocationacross data elements 620 are turned on. The user interface 1180 shows alist of available objectives 1115 with multiple KPIs per objective.

As discussed above, the objectives panel 1117 allows users ofapplication 140 to prioritize KPIs 1156 that have been selected asobjectives for marketplace inventories. KPIs 1156 may be dragged fromthe available objectives 1115 into the objectives panel 1117. If noobjectives are selected, a predetermined default, such as priceoptimization, may be automatically selected.

The available objective may be organized into KPI groups under a singleobjective category. For example, the Brand Lift KPI 1143 and CompletionRate KPI 1140 may fall under the Brand Awareness objective 1136.Similarly, the Purchase Intent Lift KPI 1153 and Click-through Rate(“CTR”) 1150 KPIs may fall under the consideration objective 1146. Auser may be prohibited from selecting multiple KPIs from the sameobjective category. For example, if a user has selected Completion Rate1140 as an objective, the user may be prohibited from also selectingBrand Lift 1143 as an objective, as a KPI from the Brand Awarenessobjective 1136 has already been selected.

Once a KPI has been selected as an objective, associated data may bepopulated in the objectives panel 1117. For example, if In TargetImpressions 1133 has been selected, data may be obtained and displayedregarding the target group that the user may have previously input in aforecast measurement provider. The user may or may not be able to editthe particular constraints of the KPI, in this case age and gender dataof the target group.

Certain KPIs 1156 may be compatible for mutual optimization and certainothers may not, even if KPIs are classified under different objectives.When a user selects a KPI as an objective, a compatibility may bedetermined with other KPIs. Non-compatible KPIs may be grayed out orotherwise made unavailable for selection as a second objective. Forexample, In Target Impressions 1133, Completion Rate 1140, and CTR 1150may be able to be selected as objectives and optimized simultaneously.Similarly, Brand Lift 1143 and Purchase Intent Lift 1153 may be able tobe both selected as objectives and optimized simultaneously. However, ifa user selects In Target Impressions 1133 as an objective, Brand Lift1143 and Purchase Intent Lift 1153 may be determined incompatible, andgrayed out or otherwise restricted. A tool tip or other communication tothe user may be presented to explain the incompatibility, for example,if a user attempts to select a grayed out KPI. In this manner, a usermay be able to optimize along a central theme, such as brand awareness,or purchase intent. Thus, techniques presented herein may enabletheme-based optimization simultaneously with ranked objective-basedoptimization.

Once objectives are selected in the objectives panel 1117, a user may beable to drag the objectives to reorder them. For example, a user may beable to drag the secondary objective above the primary objective in theobjectives panel 1117 in order to turn the secondary objective into theprimary objective. When new objectives are selected or existingobjectives are reordered, the delivery and audience forecast panel 825may be updated.

The number of objectives that may be selected for simultaneousoptimization may be limited in the objectives panel 1117. For example, amaximum of three objectives might be selectable in the objectives panel1117. In other embodiments, more than three objectives may be designatedby a user.

The application 140 may also utilize product flags which allow theenabling of various bundles of software features. For example, if thefeatures associated with the objective-centric workflow have beenenabled, the user may see the objectives panel 1117, but may berestricted from being able to optimize for Brand Lift 1143 or PurchaseIntent Lift 1153. If the additional features associated with brand liftoptimization are enabled, the user may be able to optimize for BrandLift 1143 and Purchase Intent Lift 1153, but these features mayotherwise be restricted. Some features or sets of features may rely onother sets of features to be enabled in order for themselves to beaccessible. If the features associated with the objective-centricworkflow have not been enabled, enabling brand lift optimization mayhave no effect, as the objectives panel 1117 would not be visible toenact brand lift optimization features.

Continuous optimizations may be performed throughout the campaign. Thiscontinuous optimization may be focused on serving an audience withcertain user behavior. For example, if the in-target optimization for adata element is with females aged 18-49, the system may focus on servingto an audience with viewers having behavior similar to or related to thefemales aged 18-49 age/gender group.

The top optimization schemes may be tested and identified includingfrequency, sites, segments, etc. The top optimization schemes may bereused utilizing the optimum frequency of data element distribution,optimum sites for data element distribution, and/or optimum consumersegments, for example. The system may be able to improve performance byincreasing resources for a delivered high-performance inventory, andreducing resources to a delivered low-performance inventory. The systemmay first calculate a realistic or average KPI for each optimizationobjective based on the forecasting. The system may then calculate atarget KPI for each optimization based on both the realistic KPI and theKPI value set by the user. The system may then reward any inventory witha KPI better than the target KPI, and may penalize any inventory with aKPI worse than the target.

The system may also be able to constrain delivery within certain sitesand within certain segments. This may be aided with user input.

With these techniques and others presented herein, at least someembodiments presented herein allow having multiple objectives in amanner which is both stable and scalable. Scalability may be a barrierto optimization, since a user may need to manually generate thesite/segment list for each data element. Further, some of the selectedobjectives may conflict. In the real world, for example, high completionrate data element inventories often have low click rates, since a clickusually redirects the user to an external website before the video iscompleted. If the advertiser wishes to optimize for both CTR and CRsimultaneously, the objectives may conflict. Another challenge isadapting to recent changes. During the flight of a data elementcampaign, the performance of a strategy may deteriorate as it ages.

In examples used above, KPIs may also be used that provide brand lift.Brand lift data may be generated internally to application 140, orimported from a third party data service. Typical equations to generatebrand lift metrics may include: Incremental Brand Impact (“IBI”)=BrandAwareness Lift*Reach; Brand Cost Per Action (“Brand CPA”)=Spend/IBI;Brand Impact per Thousand Impressions (BPM)=IBI/Impressions/1000; BrandROI (“BROI”)=IBI/Spend. The brand ROI may be similar to the ROIdetermination, except that IBI is in the numerator. IBI may quantify theviewer perception impact, which may be derived via surveys, coupled withthe scale of the impact. For example, survey providers may measure therise, or lift, in consideration of purchasing a product by conducting asurvey. This lift may be captured as a percentage. Once the average liftis determined, the average lift may be multiplied by the estimatednumber of impacted viewers to obtain the IBI.

Certain KPIs 1156 may engage different external sources of data. Forexample, brand lift optimization features such as Brand Lift 1143 andPurchase Intent Lift 1153 may be enabled by receiving data via a thirdparty data provider, such as via an application program interface(“API”).

The application 140, based on the rankings of objectives in theobjectives panel 1117, may accord a greater priority to meeting theprimary objective over the secondary objective, and so on. The higherpriority objective may mean that the optimization algorithm will attemptto achieve the goal with more resources, such as financial resources.However, the lower priority objectives may still receive resources. Asan example model, a data element bid price for impressions may beexpressed as follows:

Price=Base+ctr_value (eCTR−ctr_target)+cr_value (eCR−cr_target)

where example objectives used are CTR and Completion Rate.

The optimization system may generate a value of Base, ctr_value,ctr_target, cr_value, cr_target, and perhaps others, if optimization isenabled with one or more objectives. The Base, ctr_value, and cr_valuemay reflect resources allocated to each objective. The Base variable maybe used to achieve a delivery goal objective. The ecpm may represent thesum of all resources allocated, and may be set by the user. Objectiveswith a higher priority may be allocated more resources, or a largerportion of existing resources, if the goals are not met.

For each opportunity in the market, an eCTR and eCR may be determined.These variables may be based on the information received about theadvertising opportunity, and may further be used with a machine learningmodel.

As an example, values of these variables may be Base=1, ctr_value=1000,cr_value=5, ctr_target=0.005, cr_target =0.6. For a first opportunitywith eCTR=0.1 and eCR=0.8, the system may be willing to bid 1+5+1=$6 cpmfor it. For a second opportunity with eCTR=0.2 and eCR=0.9, the systemmay be willing to bid 1+15+1.5=$17.50 cpm for it. Thus, the formula mayreward the high performance inventory with a higher bid price. Theassumption here is that the higher the bid price the higher the win ratein ad space auctions.

The ctr_value and cr_value variables may correspond to factors thatcapture the relative priorities of each objective, and may act as a“priority multiplier.” For example, if the CTR is the primary objective,the ctr_value variable would be higher than the cr_value variable. Thectr_target may be based on the CTR goal set by the user, and based onthe available inventory. The ctr_target and cr_target variablescorrespond to the optimization algorithm and/or the user's demand forthe resource. Variables ctr_target and cr_target may be based on severalfactors, including available supply, average performance, and the goalinput by the user in panel 1117. The ctr_target/cr_target calculationrepresents a more realistic explanation of the given objectives.Variables eCTR and eCR may be calculated online for each incoming bidopportunity.

A value corresponding to the goal Click Through Rate may be subtractedfrom the forecasted or expected Click Through Rate. The resulting valuemay be multiplied by a priority multiplier to calculate a Click ThroughRate result. Similary, a value corresponding to the goal Completion Ratemay be subtracted from the forecasted or expected Completion Rate. Theresulting value may be multiplied by some other priority multiplier tocalculate a Completion Rate result. The Click Through Rate result andCompletion Rate result may be added together along with potentially abase value to determine a price per impression.

As a result of these techniques, the optimization algorithm may beconfigured to tolerate paying a higher price for higher expected ClickThrough Rate, and a higher price for a higher expected Completion Rate.The optimization algorithm may be considered not binary in two senses.The higher priority objectives do not necessarily cut off resourcesentirely from lower priority goals. The priority multipliers for eachobjective are not necessarily zero. The optimization algorithm may alsobe configured to tolerate bidding on spots for data elements with lowerperformance, but the price will be low.

Although Click Through Rate and Completion Rate are discussed above asexamples of primary and secondary objectives, any KPI could be optimizedwith the above techniques, and more than two objectives could be used.

The optimization techniques discussed herein allow for forecastingsupply in order to maximally achieve the stated objectives. Determiningthe price to be paid for promotion opportunities may be performed morecarefully than prior techniques, and may take all objectives intoconsideration. Embodiments presented herein do not necessarily involveexecuting an optimizer state switch (for example, by switching theprimary and secondary objective). As a result, transitions are smootherwhich enhances optimizer stability over prior algorithms.

FIG. 12 is a flow diagram illustrating an example method for optimizingdata element usage based on multi-touch attribution data according totechniques presented herein. The method may utilize a demand-sideplatform (“DSP”) environment 1201 and a multi-touch attribution (“MTA”)environment 1203, which may operate on a plurality of servers, forexample, a publisher server 110 and an MTA server 125, respectively.Alternatively, the DSP and MTA environments may be integrated and/or runon the same server or device, such as on the publisher server 110 or theclient 130. A user, who may be a privileged user and/or advertiser, maylog into the MTA environment at step 1205. The one or more MTAenvironments 1203 may be administered by a third party. The one or moreMTA environments 1203 may also provide event and/or conversion data tothe DSP environment 1201 and/or client 130. For example, the one or moreMTA environments 1203 may provide multi-touch attribution data to theone or more DSP environments 1201.

As stated above, the MTA environment 1203 may provide event data such asconversion data to the MTA environment 1201. As step 1210, a user mayconfigure these events in the MTA environment. Tracking of conversion orother events may be configured using application 140, which may, forexample, execute in or access the DSP environment 1201. A user may entera tracking URL for each data element or creative, which may result in atracking pixel or other tracking code being implemented at the dataelement and/or creative level. After configuration, at step 1215, eventtags may be applied to tracked pages, such as data elements embedded inweb pages, or other tracked entities. The event tags may comprisecomputer code which provide the ability to raise and log events, such asconversion events, associated with user behavior when interacting withdata elements or other tracked entities. Once one or more event tags, orsome other data tracking functionality, are applied, at step 1220 theMTA application, which may be a portion of application 140, may provideevent information for storage and/or processing. This event informationmay relate to impressions provided to users, user selection of dataelements, such as mouse clicks of an advertisement, conversion events,such as a user completing a purchase of a product, a user reviewing anarticle related to a product, etc.

In the DSP environment 1201, an MTA application, operating in the MTAenvironment, or other features capable of receiving event informationmay be enabled at step 1225, for example by a user such as anadvertiser. The MTA application may further be configured at step 1230.For example, a user may be required to enter credentials such as email,user name, and/or password to create a data connection with the MTAenvironment 1203. The data connection may occur over the network 190,and may occur via an application programming interface (API), althoughother types of data connections may be used. Once configured, the dataconnection may allow event information to be provided by the MTAenvironment 1203 for use in the DSP environment 1201.

When a user creates a data element group and/or campaign for which eventinformation is tracked, at step 1235 the group and/or campaign may beconfigured in the DSP environment 1201. Events to be tracked for dataelement optimization may be selected at step 1240. Types of events whichmay be optimized will be discussed further below, but may include, forexample, a home page click event, a sign up click event, a free trialsign up event, etc. Optimization may be performed according totechniques discussed elsewhere herein. When the campaign or data elementgroup launches, at step 1245, listings or other indicators of dataelements served or made “live” may be provided to the MTA environment1203 at step 1250.

At step 1255, the MTA application may receive impression datacorresponding to tracked pages or events. The MTA application mayfurther receive and process event data, such as numbers of completedsales of a product or service associated with one or more data elementsor the campaign, and/or related to events such as a home page clickevent, sign up click event, free trial sign up event, etc. At step 1265,one or more models may be applied to process impression and/or eventdata to generate revenue information, such as return on investment(“ROI”) information. The ROI information, at step 1270, may be used bythe application 140 for optimizing data element usage, and may bedisplayed by one or more user interfaces 150. The ROI information may betransferred from the MTA environment to the DSP environment via API,including via data push. The ROI information may also be determined inthe DSP environment 1201 using data obtained from the MTA environment1203. This may include revenue information by day or other time period,revenue information by data element, data element group, website, event,or by campaign, etc. The ROI information may be determined for each dataelement, data element group, etc., based on the cost and revenue orprofit generated post attribution. The user interfaces 150 may also useor display data related to impressions, conversions, and/or any otherevent data. The event data, such as ROI information, may also be usedfor resource optimization, at step 1275, using optimization techniquesdiscussed elsewhere herein.

These techniques allow for optimizing to maximize conversion byutilizing MTA data, rather than optimization by data that merelycomprises last view or last click events. The application 140 may waituntil a statistically significant amount of event data, such as ROIdata, has accumulated before proceeding with optimization based on theROI or other event information. The MTA environment 1203 may also signalthe DSP environment 1201 when a statistically significant amount of ROIinformation has been obtained for a given data element, data elementgroup, and/or campaign. The application 140 may also display a notice toa user when a statistically significant amount of event data has beengathered. The optimization may be performed on the budget, based on theROI/conversion data and/or other optimization objectives discussedherein. The application 140 may also provide a user interface 150 thatwould allow a user to rank objectives, including a conversion objective,for optimization, as will be discussed further below. MTA data such asROI data may be provided by the MTA environment 1203 on a daily basis,by some other predetermined time interval, and/or on demand.

While the steps discussed in relation to method 1200 are discussed in acertain order, many of these steps may be performed out of this order,as would be clear to a person of ordinary skill. For example, the MTAapplication may be enabled at step 1225 prior to conversion tags beingapplied to tracked pages, as in step 1215. Certain users may berestricted from enabling or disabling the MTA application. For example,a user may need to be an administrator before being able the enable theMTA application. Additional conversion events may be configured at step1210 at any point in time. Campaigns utilizing MTA may be configured atstep 1235 at any point in time. Certain steps in relation to method 1200also may be optional. For example, the method 1200 may collectimpression data at step 1255, while not collecting or utilizingconversion data, as in step 1260. As another example, while a user mayreceive ROI information at step 1270, optimizing objectives using thisinformation may be optional. Further, optimization of objectives may beperformed using any event-related data, not just ROI data.

FIG. 13 is a flow diagram illustrating an example method for optimizingdata element usage based on multi-touch attribution data according totechniques presented herein. Optimization using ROI data to maximizeconversion, or using any other event-related data, may be unavailableunless a number of prerequisites are satisfied. At steps 1305 and 1310,if MTA is either not enabled and/or not configured, events may not beable to be stored. At steps 1315 and 1320, if the MTA data is notstatistically significant, the user may be required to wait until thedata pool reaches statistical significance. At steps 1325 and 1330, ifoptimization 805 is not enabled, the client 130, MTA server 125,publisher server 110, and/or data element server 120 may continue to logMTA data such as conversion information. At step 1330, if theserequirements are met, objectives may be optimized using MTA data such asROI information. Although the steps depicted in FIG. 13 are discussed ina certain order, the ordering may vary, and one or more steps may beoptional.

FIG. 14 is an example user interface enabling the selection ofobjectives associated with groups of data elements according totechniques presented herein. In addition to objectives discussedelsewhere herein, a user may also be able to designate MTA-relatedobjectives, such as a conversion maximization objective 1405.MTA-related objectives such as conversion objectives may be designatedas a primary objective, secondary objective, etc. The user interface1400 may support enabling and optimizing against MTA data, such as ROIdata, for example by data element and/or data element event. One or moreevent types 1410 may be selected for optimization, along with a daterange 1415 of these events. Once selected, the conversion objective 1405may be displayed along with any other objectives in the objectivesdisplay at 1432. As an example, a user may wish to optimize for revenueinformation for the last six months, and may select a date in the daterange 1415 accordingly. A default date in the date range 1415 may be tothe earliest date for which a conversion event was received. TheMTA-related event data may be received via pixels embedded within oraround displayed data elements. Based on the received event data,performance projections may be forecast in the user interface 1400, suchas in the delivery and audience forecast areas 1420. For example, actualto-date ROI 1425 and/or forecasted ROI 1430 may be displayed. The actualROI 1425 may be calculated as (Revenue-Spent)/Spent. The forecasted ROI1430 may be determined based on revenue, event selected, and/or dataelement. The forecasted ROI 1430 may further be determined based on theselected events 1432 for tracking, and optimization may occur againstthese selected events. When a user selects a conversion objective 1405,all other objectives may be greyed out or otherwise be made unavailable,although the user may be able to add or remove event types 1410 to theconversion objective event list 1432. To add an additional objective, anadditional objective group may need to be added, such as purchase funnelobjectives 1434, which may be added as a secondary objective, using theexample user interface of FIG. 14.

FIG. 15 is an example user interface enabling the selection ofelectronic events associated with groups of data elements according totechniques presented herein. When a user selects one or more event types1410, user interface 1500 may appear and allow for the selection ofevents to track to meet MTA-related objectives, such as optimizing forROI. Users may be able to search for conversion events at a search box1505. Events which may be selectable for optimization may include 2.wapp complete 1510, an email signup event 1515, a website entrance popupsignup event 1520, a website exit popup signup event 1525, a selectionof free trial event 1530, a selection of indeed sponsorship event 1535,and a selection of indeed sponsorship invalid code event 1540

FIG. 16 is a flow diagram illustrating an example method 1600 forpriority-based optimization of distribution of resources for dataelements, consistent with the user interfaces described above withrespect to FIGS. 11A-11C, according to techniques presented herein. Atstep 1605, a selection of a first objective and a second objective maybe received, the first objective and second objective comprising goalsbeing associated with distribution of a plurality of data elements. Forexample, in FIG. 11C, a user may designate In Target Impressions 1130 asa primary objective, and Completion Rate 1140 as a secondary objective.At step 1610, an indication that the first objective has a higherpriority than the second objective may be received. For example, in FIG.11C, in the objective panel 1160, a user may designate the In TargetImpressions objective higher than the Completion Rate objective. At step1615, a first goal metric associated with the first objective and asecond goal metric associated with the second objective may be received.For example, a user may designate that the In Target Impressions bedirected at females age 18-45, and the Completion Rate goal be greaterthan or equal to 90%. At step 1620, a first forecasted metric based onthe first goal metric associated with the first objective may bedetermined. For example, the application 140 may forecast the ability tomeet the In Target Impressions goal of the In Target Impressionsobjective. At step 1625, a second forecasted metric based on the secondgoal metric associated with the second objective may be determined. Forexample, the application 140 may forecast the ability to meet theCompletion Rate goal for the Completion Rate objective. At step 1630,resources for the distribution of a plurality of data elements may beallocated based on the first goal metric, the second goal metric, thefirst forecasted metric, the second forecasted metric, and theindication that the first objective has a higher priority than thesecond objective. For example, the application 140 may determine abudget and/or bid price to dedicate towards the promotion of certaindata elements based on the goal metrics, the forecasted metrics, and theobjective panel 1117 rankings.

Techniques presented herein may provide a differentiated buying toolallowing one or more owners 305 to purchase space for promotional dataelements 315 that may eliminate substantial manual work and providereal-time optimal allocation of resources to data elements. In addition,the optimization algorithm may work recursively up or down the objecthierarchy, thus increasing the efficiency of optimization according toobjectives and/or constraints. While many settings discussed herein maybe able to be set at the data element group level, these same settingsmay be set at the campaign level and/or data element level, unlessexpressly stated otherwise herein. More generally, any setting which maybe configured at any level of the object hierarchy may also beconfigured at any other level of the object hierarchy, unless expresslystated otherwise herein. All user interfaces shown herein, orcombinations thereof, may be present in various embodiments, and may bepresented to one or more users. All features discussed herein may haveassociated security requirements before they may be used. For example,different users of the application may have different levels ofprivileges, allowing them to access differing features of theapplication. In addition, many steps of techniques discussed herein aredisclosed in a particular order. In general, steps discussed may beperformed in any order, unless expressly stated otherwise.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs forsystems and a methods for optimizing data element usage through thedisclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

1-20. (canceled)
 21. A computer-implemented method for optimizingdistribution of resources for data elements at an optimization server,comprising: receiving, at the optimization server, a selection of afirst objective and a second objective, the first objective and secondobjective comprising goals associated with distribution of a pluralityof data elements stored in a database; receiving, at the optimizationserver, an indication that the first objective has a higher prioritythan the second objective; disallowing, at the optimization server,selection of one or more key performance indicators as the secondobjective at least based on the first objective; receiving, at theoptimization server, a first goal metric associated with the firstobjective and a second goal metric associated with the second objective;determining, at the optimization server, a first forecasted metric ofthe first objective based on the first goal metric; determining, at theoptimization server, a second forecasted metric of the second objectivebased on the second goal metric; and allocating, at the optimizationserver, resources for the distribution of a plurality of data elements,the plurality of data elements being retrieved from the database, theallocating being based on the determined first forecasted metric, thedetermined second forecasted metric, and the indication that the firstobjective has a higher priority than the second objective.
 22. Thecomputer-implemented method of claim 21, wherein receiving a selectionof a first objective further comprises receiving a selection of one of aplurality of key performance indicators.
 23. The computer-implementedmethod of claim 21, wherein allocating resources for the distribution ofthe plurality of data elements comprises determining an impression priceassociated with publication of one or more of the plurality of dataelements, wherein the determined impression price corresponds to thedegree to which the first goal metric and second goal metric are to beachieved.
 24. The computer-implemented method of claim 21, wherein theoptimization server comprises one or more servers, and the databasecomprises one or more databases, and further comprising: receiving, atthe optimization server, a selection of a third objective; receiving, atthe optimization server, a third goal metric associated with the thirdobjective; determining, at the optimization server, a third forecastedmetric based on the third goal metric; and allocating, at theoptimization server, resources for the distribution of a plurality ofdata elements based on the first forecasted metric, the secondforecasted metric, the third forecasted metric, and the indication thatthe first objective has a higher priority than the second objective. 25.The computer-implemented method of claim 21, further comprising:determining, at the optimization server, a theme associated with thefirst objective; and disallowing selection of one or more keyperformance indicators as the second objective at least based on thefirst objective by disallowing, at the optimization server, selection ofany key performance indicator as the second objective that is notassociated with the determined theme.
 26. The computer-implementedmethod of claim 21, wherein the first objective corresponds to anobjective category associated with a plurality of key performanceindicators, and further comprising: receiving, at the optimizationserver, a selection of one of the plurality of key performanceindicators as the first objective; and disallowing selection of one ormore key performance indicators as the second objective at least basedon the first objective by disallowing, at the optimization server,selection of any remaining key performance indicators associated withthe objective category as the second objective.
 27. Thecomputer-implemented method of claim 21, further comprising:determining, at the optimization server, a priority multiplier based onthe indication that the first objective has a higher priority than thesecond objective, wherein the priority multiplier is based on the degreeof higher priority that the first objective has over the secondobjective; and applying, at the optimization server, the prioritymultiplier when allocating resources for the distribution of a pluralityof data elements.
 28. The computer-implemented method of claim 21,further comprising: receiving, at the optimization server, amodification in the selection of the first objective or the first goalmetric; and allocating, at the optimization server, resources for thedistribution of the plurality of data elements based on the modificationin the selection of the first objective or the first goal metric.
 29. Asystem for optimizing distribution of resources for data elements,comprising: a data storage device storing instructions for optimizingdistribution of resources to data elements; and a processor configuredto execute the instructions to perform a method including: receiving aselection of a first objective and a second objective, the firstobjective and second objective comprising goals associated withdistribution of a plurality of data elements; receiving an indicationthat the first objective has a higher priority than the secondobjective; disallowing selection of one or more key performanceindicators as the second objective at least based on the firstobjective; receiving a first goal metric associated with the firstobjective and a second goal metric associated with the second objective;determining a first forecasted metric of the first objective based onthe first goal metric; determining a second forecasted metric of thesecond objective based on the second goal metric; and allocatingresources for the distribution of a plurality of data elements, theplurality of data elements being retrieved from the database, theallocating being based on the determined first forecasted metric, thedetermined second forecasted metric, and the indication that the firstobjective has a higher priority than the second objective.
 30. Thesystem of claim 29, wherein receiving a selection of a first objectivefurther comprises receiving a selection of one of a plurality of keyperformance indicators.
 31. The system of claim 29, wherein allocatingresources for the distribution of the plurality of data elementscomprises determining an impression price associated with publication ofone or more of the plurality of data elements, wherein the determinedimpression price corresponds to the degree to which the first goalmetric and second goal metric are to be achieved.
 32. The system ofclaim 29, wherein the optimization server comprises one or more servers,and the database comprises one or more databases, and wherein theprocessor is further configured for: receiving a selection of a thirdobjective; receiving a third goal metric associated with the thirdobjective; determining a third forecasted metric based on the third goalmetric; and allocating resources for the distribution of a plurality ofdata elements based on the first forecasted metric, the secondforecasted metric, the third forecasted metric, and the indication thatthe first objective has a higher priority than the second objective. 33.The system of claim 29, wherein the processor is further configured for:determining a theme associated with the first objective; and disallowingselection of one or more key performance indicators as the secondobjective at least based on the first objective by disallowing selectionof any key performance indicator as the second objective that is notassociated with the determined theme.
 34. The system of claim 29,wherein the first objective corresponds to an objective categoryassociated with a plurality of key performance indicators, and theprocessor is further configured for: receiving a selection of one of theplurality of key performance indicators as the first objective; anddisallowing selection of one or more key performance indicators as thesecond objective at least based on the first objective by disallowingselection of any remaining key performance indicators associated withthe objective category as the second objective.
 35. The system of claim29, wherein the processor is further configured for: determining apriority multiplier based on the indication that the first objective hasa higher priority than the second objective, wherein the prioritymultiplier is based on the degree of higher priority that the firstobjective has over the second objective; and applying the prioritymultiplier when allocating resources for the distribution of a pluralityof data elements.
 36. The system of claim 29, wherein the processor isfurther configured for: receiving a modification in the selection of thefirst objective or the first goal metric; and allocating resources forthe distribution of the plurality of data elements based on themodification in the selection of the first objective or the first goalmetric.
 37. A non-transitory computer-readable medium storinginstructions that, when executed by a processor, cause the processor toperform a method of optimizing distribution of resources for dataelements, the method including: receiving a selection of a firstobjective and a second objective, the first objective and secondobjective comprising goals associated with distribution of a pluralityof data elements; receiving an indication that the first objective has ahigher priority than the second objective; disallowing selection of oneor more key performance indicators as the second objective at leastbased on the first objective; receiving a first goal metric associatedwith the first objective and a second goal metric associated with thesecond objective; determining a first forecasted metric of the firstobjective based on the first goal metric; determining a secondforecasted metric of the second objective based on the second goalmetric; and allocating resources for the distribution of a plurality ofdata elements, the plurality of data elements being retrieved from thedatabase, the allocating being based on the determined first forecastedmetric, the determined second forecasted metric, and the indication thatthe first objective has a higher priority than the second objective. 38.The non-transitory computer-readable medium of claim 37, whereinreceiving a selection of a first objective further comprises receiving aselection of one of a plurality of key performance indicators.
 39. Thenon-transitory computer-readable medium of claim 37, wherein allocatingresources for the distribution of the plurality of data elementscomprises determining an impression price associated with publication ofone or more of the plurality of data elements, wherein the determinedimpression price corresponds to the degree to which the first goalmetric and second goal metric are to be achieved.
 40. The non-transitorycomputer-readable medium of claim 37, wherein the optimization servercomprises one or more servers, and the database comprises one or moredatabases, the method further comprising: receiving a selection of athird objective; receiving a third goal metric associated with the thirdobjective; determining a third forecasted metric based on the third goalmetric; and allocating resources for the distribution of a plurality ofdata elements based on the first forecasted metric, the secondforecasted metric, the third forecasted metric, and the indication thatthe first objective has a higher priority than the second objective.